SeRSy 2012

Scope

People generally need more and more advanced tools that go beyond those implementing the canonical search paradigm for seeking relevant information. A new search paradigm is emerging, where the user perspective is completely reversed: from finding to being found. Recommender Systems may help to support this new perspective, because they have the effect of pushing relevant objects, selected from a large space of possible options, to potentially interested users. To achieve this result, recommendation techniques generally rely on data referring to three kinds of objects: users, items and their relations.

Recent developments of the Semantic Web community offer novel strategies to represent data about users, items and their relations that might improve the current state of the art of recommender systems, in order to move towards a new generation of recommender systems which fully understand the items they deal with.

More and more semantic data are published following the Linked Data principles, that enable to set up links between objects in different data sources, by connecting information in a single global data space: the Web of Data. Today, Web of Data includes different types of knowledge represented in a homogeneous form: sedimentary one (encyclopedic, cultural, linguistic, common-sense) and real-time one (news, data streams, ...). This data might be useful to interlink diverse information about users, items, and their relations and implement reasoning mechanisms that can support and improve the recommendation process.

The challenge is to investigate whether and how this large amount of wide-coverage and linked semantic knowledge can be automatically introduced into systems that perform tasks requiring human-level intelligence. Examples of such tasks include understanding a health problem in order to make a medical decision, or simply deciding which laptop to buy. Recommender systems support users exactly in those complex tasks.

The primary goal of the workshop is to showcase cutting edge research on the intersection of Semantic Technologies and Recommender Systems, by taking the best of the two worlds. This combination may provide the Semantic Web community with important real-world scenarios where its potential can be effectively exploited into systems performing complex tasks.

Invited Speaker

Bio: Ora Lassila is a Principal Technologist in Nokia's Big Data Analytics group where he worries about building systems for cataloguing and describing the multiple (big) data sets that make up Nokia's multi-petabyte data asset. Earlier, Dr. Lassila was a Research Fellow at Nokia Research Center where he pioneered the idea of the Semantic Web. He also dabbled in venture capitalism and has held research positions at MIT, CMU and Helsinki University of Technology. He holds a Ph.D in CS from the Helsinki University of Technology.

Size does not matter if your data is in a silo

Abstract: The advent of "big data" has afforded many opportunities for the discovery of interesting facts and phenomena about the world where this data was collected. Lots of excitement surrounds the systems and platforms which are used in processing big data (and indeed, which are capable of the scale needed). Some of the technical optimizations needed for scaling up processing (e.g., NoSQL, key/value databases) have resulted in "weaker" or less accessible data models, and consequently tend to emphasize the notion of "siloed" data. To mitigate this, we need stronger effort on how data is described, both in terms of operational parameters, provenance, workflows, and rich data models. Semantic Web technologies are well suited to capturing all the metainformation needed, regardless of the physical formats and storage solutions used for the actual data.